People's Mental States Research Articles

Analysis of the Affixoid "霸" in Modern Chinese

The affixoid is a linguistic unit in modern Chinese that lies in the middle ground between roots and affixes, and research on the ontology of class affixes, a class of class affixes and a specific class of affixes has already yielded some results. In this paper, we have selected "霸", which has received relatively little attention from previous researchers. In the course of the paper, the main methods used are documentary research, combination of description and interpretation, and corpus analysis. The paper begins with a definition of the affixoid "霸" in the light of previous research on the definition of affixes, explaining the basic features of the affixoid "霸" and classifying its derivatives. This is followed by a specific analysis of the class affixoid "霸" and its derivatives in terms of syntax, semantics and pragmatics. The grammaticalization process of the affixoid "霸" is then analyzed in order to show how people's mental states and situations change.

An action-observation method for studying social perception: a mini-review.

An important aspect of any social interaction involves inferring other people's mental states, intentions, and their likely next actions, by way of facial expression, body posture, eye gaze, and limb movements. An actor's production of actions during social interactions and the observer's perception of these actions are thus closely linked. In this review, we outline an action-observation methodology, which not only allows for separate analyses of production and perception, but also promotes the study of the dynamic interaction between these two sides of every social exchange. We review two lines of research that have benefited from its application. The first line focuses on individuals performing tasks alone and the observation of their actions by other individuals in order to make inferences about their attentional states. The second line of study focused on pairs of individuals performing collaborative tasks in naturalistic settings and the observation of these performances by other individuals. We offer several suggestions for how this methodology can be extended to improve on the limitations of the present studies, as well as some suggestions of how to use this methodology to venture into new territory. Our aim is to inspire future research applications of this methodology in order to advance our understanding of social action production and perception.

Theory of mind in chronic migraine with medication overuse assessed with the MASC

Theory of Mind (ToM) is the ability to infer one's own and others' mental states. Growing research indicates that ToM is impaired in Chronic Migraine with Medication Overuse (CM + MO). However, the research in this field has been conducted using static scenario-based tasks, often failing to test mentalization in everyday situations and measuring only performance accuracy. We filled this gap by administering the Movie for the Assessment of Social Cognition (MASC) to subjects with CM + MO compared to episodic migraine (EM). This test allows us to assess both affective and cognitive ToM and which, in addition to being accurate, also analyzes the type of error in attribution of mental states, distinguishing between hypo-mentalization and hyper-mentalization. Thirty patients suffering from CM + MO and 42 from EM were enrolled. Results showed that CM + MO patients were less accurate in mental state attribution than EM. In addition, compared to EM, CM + MO individuals were more impaired in the affective ToM dimensions and committed more errors of hypo-mentalization. In conclusion, the application of MASC in patients with CM + MO allowed for the detection of an alteration in their ability to correctly draw conclusions about other people's mental states. This latter contributes critically to appropriate social reactions and also, possibly, to satisfactory social interactions.

Influence of transient emotional episodes on affective and cognitive theory of mind.

Our emotions may influence how we interact with others. Previous studies have shown an important role of emotion induction in generating empathic reactions towards others' affect. However, it remains unclear whether (and to which extent) our own emotions can influence the ability to infer people's mental states, a process associated with Theory of Mind (ToM) and implicated in the representation of both cognitive (e.g. beliefs and intentions) and affective conditions. We engaged 59 participants in two emotion-induction experiments where they saw joyful, neutral and fearful clips. Subsequently, they were asked to infer other individuals' joy, fear (affective ToM) or beliefs (cognitive ToM) from verbal scenarios. Using functional magnetic resonance imaging, we found that brain activity in the superior temporal gyrus, precuneus and sensorimotor cortices were modulated by the preceding emotional induction, with lower response when the to-be-inferred emotion was incongruent with the one induced in the observer (affective ToM). Instead, we found no effect of emotion induction on the appraisal of people's beliefs (cognitive ToM). These findings are consistent with embodied accounts of affective ToM, whereby our own emotions alter the engagement of key brain regions for social cognition, depending on the compatibility between one's own and others' affect.

Harmony in Healing: Exploring the Potentials and Obstacles of Psionic Medicine in Integrative Healthcare

Incorporating psychological considerations is an essential component in both the practice of medicine and the management of various complex situations. Psychological aspects can play a role in the etiology, diagnosis, and course of treatment. Psionic medicine is an approach to healing that combines traditional medicine with radiesthesia to address the root causes of illness without using any artificial chemicals. Psionic medicine ushers in a new era as it can potentially maintain and restore patients' health. Additionally, it significantly adds to the range of treatment options developed through biochemical medicine. Psionic medicine emphasizes applying subtle "informational" inputs that affect the patient's biofield, as opposed to chemistry and surgical intervention. Psionic medicine primarily emphasizes identifying and treating the underlying causes of disease. The "theory of mind" and heuristic methodology are at the core of this endeavor. Even though the "theory of mind" gives humans the ability to reason about their own and other people's mental states, the heuristic method is the approach to problem-solving that requires the least amount of mental effort. The heuristic method is based on empirical evidence. When using psionics, several aspects of the patient's psychic reality and pathogenic beliefs, the affected system, the experience of feeling, emotion, mood, and so on, are all investigated.

Scene construction ability in neurotypical and autistic adults.

People with autism spectrum conditions (ASC) have difficulties imagining events, which might result from difficulty mentally generating and maintaining a coherent spatial scene. This study compared this scene construction ability between autistic (N = 55) and neurotypical (N = 63) adults. Results showed that scene construction was diminished in autistic compared to neurotypical participants, and participants with fewer autistic traits had better scene construction ability. ASC diagnosis did not influence the frequency of mentions of the self or of sensory experiences. Exploratory analysis suggests that scene construction ability is associated with the ability to understand our own and other people's mental states, and that these individual-level preferences/cognitive styles can overrule typical group-level characteristics.

Theory of Mind, Roles, and the Development of Emotion Regulation in Early Childhood

The ability to regulate children's emotions is the basis for psychosocial development which is the key to future success, and they can quickly adapt to other people or situations that may not always be comfortable for them. Theory of mind (ToM) is a construct used to describe the ability to interpret other people's mental states, which then develops into the ability to empathize. This study examines how the theory of mind contributes to the development of emotion regulation in children aged 4, 5, and 6 years. This research used a quasi-experimental design to find the effect of ToM stimulation on children's emotional regulation. 109 respondents were selected using a purposive sampling technique. The scales used in this research are the PreBers scale to examine children's emotional regulation and the ToM scale. The research results showed that the influence of ToM development on children's emotional regulation was 52.4%. The results of this research highlight that the better the child acquires a Theory of Mind, the better the development of the child's emotional regulation. Furthermore, these findings are significant for early childhood education providers to develop programs to optimize ToM acquisition from childhood.
 Keywords: theory of mind, empathy, emotion regulation, children aged 4, 5, and 6 years

A Convolutional Neural Network Model to Detect Stress Levels in Electroencephalogram Signals

Chronic stress is recognized as a major contributing factor to the impairment of people's health and quality of life due to the negative impacts it causes both in personal and professional life. There is a tendency for the increasing of concerns about mental health and investments in healthcare in the coming years and this fact has motivated the conduction of several studies that aim to develop technological solutions that can be applied for monitoring of people's mental state to early detection and treatment of stress. Machine Learning and Deep Learning techniques have proven to be a promising alternative to provide technology to help solve the challenges in the medical field. This study presents a Convolutional Neural Network (CNN) model for classifying stress levels in data from electroencephalogram signals. The model was trained with a dataset generated from an experiment in which individuals were monitored by electroencephalogram while performing stress-inducing tasks. The model achieved an accuracy rate of 98.95% in predictions and presented an adequate balance between performance and computational costs. The results obtained demonstrate the potential of Deep Learning techniques as tools to aid in the health monitoring and diagnosis. Key Words: machine learning, deep learning, mental stress, electroencephalogram

Higher subjective socioeconomic status is linked to increased charitable giving and mentalizing-related neural value coding

Socioeconomic status (SES), a concept related to an individual's economic and social position relative to others, can shape social interactions like altruistic behaviors. However, little is known about the exact neurocognitive mechanisms that link SES with altruism. Our study aimed to provide a comprehensive account of the sociocognitive and neural mechanisms through which SES affects charitable giving – an important variant of human altruism. To this end, participants completed a charitable donation task while their brain activity was measured using functional magnetic resonance imaging (fMRI). We also assessed participants’ socio-cognitive ability to infer other people's mental states (i.e., mentalizing) – a driver of prosocial behavior – in an independent social task. Behaviorally, we found that both charitable giving and social cognition were status-dependent, as subjective SES positively predicted donations and mentalizing capacity. Moreover, the link between SES and charitable giving was mediated by individuals’ mentalizing capacity. At the neural level, a multivariate pattern analysis of fMRI data revealed that higher subjective SES was associated with stronger value coding in the right temporoparietal junction (rTPJ). The strength of this value representation predicted charitable giving and was linked to mentalizing. Furthermore, we observed an increased negative functional coupling between rTPJ and left putamen with higher SES. Together, increased charitable giving in higher-status individuals could be explained by status-dependent recruitment of mentalizing-related value coding and altered functional connectivity in the brain. Our findings provide insights into the socio- and neurocognitive mechanisms explaining why and when higher SES leads to prosociality, which might ultimately inform targeted interventions to promote prosocial behavior in human societies.

The effect of COVID-19 on mental health condition of people and treatment in China

COVID-19 is a virus-borne infectious disease that causes severe acute respiratory syndrome and was first found in Wuhan, China. It was widely spread all over the world and caused a huge number of deaths. Beyond that, the COVID-19 epidemic has also had a huge impact on the mental state of the population. People's mental state may become more negative. These negative mental conditions would likely trigger some unpleasant behavior. Thus, it is necessary to pay more attention to the mental effect of the pandemic. The aim of this article is to introduce the symptoms of each mental disorder separately, suggest some possible consequences when these symptoms occur, and discuss the possible factors that will influence these disorders. Then, this research will indicate some treatments and suggestions.

Mental State Prediction Using Machine Learning and EEG Signal

One of the most exciting areas of computer science right now is brain-computer interface (BCI) research. A conduit for data flow between both the brain as well as an electronic device is the brain-computer interface (BCI). Researchers in several disciplines have benefited from the advancements made possible by brain-computer interfaces. Primary fields of study include healthcare and neuroergonomics. Brain signals could be used in a variety of ways to improve healthcare at every stage, from diagnosis to rehabilitation to eventual restoration. In this research, we demonstrate how to classify EEG signals of brain waves using machine learning algorithms for predicting mental health states. The XGBoost algorithm's results have an accuracy of 99.62%, which is higher than that of any other study of its kind and the best result to date for diagnosing people's mental states from their EEG signals. This discovery will aid in taking efforts [1] to predict mental state using EEG signals to the next level.

Dress is a Fundamental Component of Person Perception.

Clothing, hairstyle, makeup, and accessories influence first impressions. However, target dress is notably absent from current theories and models of person perception. We discuss three reasons for this minimal attention to dress in person perception: high theoretical complexity, incompatibility with traditional methodology, and underappreciation by the groups who have historically guided research in person perception. We propose a working model of person perception that incorporates target dress alongside target face, target body, context, and perceiver characteristics. Then, we identify four types of inferences for which perceivers rely on target dress: social categories, cognitive states, status, and aesthetics. For each of these, we review relevant work in social cognition, integrate this work with existing dress research, and propose future directions. Finally, we identify and offer solutions to the theoretical and methodological challenges accompanying the psychological study of dress. Why is it that people often agonize over what to wear for a job interview, a first date, or a party? The answer is simple: They understand that others' first impressions of them rely on their clothing, hairstyle, makeup, and accessories. Many people might be surprised, then, to learn that psychologists' theories about how people form first impressions of others have little to say about how people dress. This is true in part because the meaning of clothing is so complex and culturally dependent. We propose a working model of first impressions that identifies four types of information that people infer from dress: people's social identities, mental states, status, and aesthetic tastes. For each of these, we review existing research on clothing, integrate this research with related work from social psychology more broadly, and propose future directions for research.

Beyond speaking: neurocognitive perspectives on language production in social interaction.

The human faculty to speak has evolved, so has been argued, for communicating with others and for engaging in social interactions. Hence the human cognitive system should be equipped to address the demands that social interaction places on the language production system. These demands include the need to coordinate speaking with listening, the need to integrate own (verbal) actions with the interlocutor's actions, and the need to adapt language flexibly to the interlocutor and the social context. In order to meet these demands, core processes of language production are supported by cognitive processes that enable interpersonal coordination and social cognition. To fully understand the cognitive architecture and its neural implementation enabling humans to speak in social interaction, our understanding of how humans produce language needs to be connected to our understanding of how humans gain insights into other people's mental states and coordinate in social interaction. This article reviews theories and neurocognitive experiments that make this connection and can contribute to advancing our understanding of speaking in social interaction. This article is part of a discussion meeting issue 'Face2face: advancing the science of social interaction'.

TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm.

COVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people's mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals' views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an accuracy rate of 95.098. It is evident from the experimental studies that the proposed approach displayed a much higher TSA performance compared to other similar approaches in the existing literature.

An Autoencoder based Emotional Stress State Detection Approach by using Electroencephalography Signals

Identifying hazards from human error is critical for industrial safety since dangerous and reckless industrial worker actions, as well as a lack of measures, are directly accountable for human-caused problems. Lack of sleep, poor nutrition, physical deformities, and weariness are some of the key factors that contribute to these risky and reckless behaviors that might put a person in a perilous scenario. This scenario causes discomfort, worry, despair, cardiovascular disease, a rapid heart rate, and a slew of other undesirable outcomes. As a result, it would be advantageous to recognize people's mental states in the future in order to provide better care for them. Researchers have been studying electroencephalogram (EEG) signals to determine a person's stress level at work in recent years. A full feature analysis from domains is necessary to develop a successful machine learning model using electroencephalogram (EEG) inputs. By analyzing EEG data, a time-frequency based hybrid bag of features is designed in this research to determine human stress dependent on their sex. This collection of characteristics includes features from two types of assessments: time-domain statistical analysis and frequency-domain wavelet-based feature assessment. The suggested two layered autoencoder based neural networks (AENN) are then used to identify the stress level using a hybrid bag of features. The experiment uses the DEAP dataset, which is freely available. The proposed method has a male accuracy of 77.09% and a female accuracy of 80.93%.

Teorija uma kod dece - preliminarno istraživanje o ulozi pamćenja i jezičkih sposobnosti kao razvojnih pokazatelja

Preschool age is a period of fundamental changes in child's ability to explicitly reason about the other people's mental states. The aim of the research was to determine the relationship between the development of theory of mind, working memory and language abilities in children with typical development, aged four to eight years. For the assessment of listed abilities several instruments were used: "Sanja and Ana" False belief task, Backward span task, Verbal memory test, Decision speed test, Toddler grammar and FONT test. The results of the research indicated statistically significant correlations between theory of mind, verbal memory, as well as some aspects of language abilities. However, as a statistically significant indicator of achievement on theory of mind tasks, only verbal memory stands out (p < .05) which together with verbal working memory predicts 26% of the variance of achievement in first-order theory of mind acquisition (F = 9.40, df = 2; p = .00) and 15% of the achievement variance in the total score on theory of mind tasks (F = 4.55, df = 2, p = .01). Research results suggest a close but intriguing relationship between theory of mind, executive functions, and language abilities.

The current mechanism and treatment methods for depression

Depression is a very common illness that negatively affects people's lives, mental states and families. This paper explains the causes of depression from both psychological and biological perspectives, finding that it is related to negative psychology and drugs, gender, genetics, brain chemistry, and even brain structure. At the same time, this paper analyzed the differences, effects and applicability of psychological, medical and physical therapy, and found that mild depression usually does not need medical intervention; Moderate and severe depression is usually treated with a combination of psychotherapy and medication; Less sensitive people can be treated with more aggressive brain stimulation.

Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review.

Theory of Mind (ToM)—the ability of the human mind to attribute mental states to others—is a key component of human cognition. In order to understand other people's mental states or viewpoint and to have successful interactions with others within social and occupational environments, this form of social cognition is essential. The same capability of inferring human mental states is a prerequisite for artificial intelligence (AI) to be integrated into society, for example in healthcare and the motoring industry. Autonomous cars will need to be able to infer the mental states of human drivers and pedestrians to predict their behavior. In the literature, there has been an increasing understanding of ToM, specifically with increasing cognitive science studies in children and in individuals with Autism Spectrum Disorder. Similarly, with neuroimaging studies there is now a better understanding of the neural mechanisms that underlie ToM. In addition, new AI algorithms for inferring human mental states have been proposed with more complex applications and better generalisability. In this review, we synthesize the existing understanding of ToM in cognitive and neurosciences and the AI computational models that have been proposed. We focus on preference learning as an area of particular interest and the most recent neurocognitive and computational ToM models. We also discuss the limitations of existing models and hint at potential approaches to allow ToM models to fully express the complexity of the human mind in all its aspects, including values and preferences.

Neuropsychological Approach on Expressed Emotion in Neurotypical and Autism Spectrum Disorder: A Path Model Analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects individual social communication with a range of restricted behaviour patterns. People with ASD will also have difficulties with social emotional reciprocity, which is not predominantly found in neurotypical individuals. Individuals with ASD have difficulty connecting with neurotypical (i.e., nonautistic) people because they fail to identify other people's emotions and mental states. Alexithymia is a personality characteristic defined by a subclinical inability to identify and explain one's own emotions. Alexithymia is defined by a significant dysfunction in emotional awareness, social attachment, and interpersonal relationships. It is distinguished by impaired emotional awareness, which has been increasing in diagnostic frequency in a variety of neuropsychiatric diseases, with notable overlap with ASD. To empirically measure the condition of alexithymia in neurotypical individuals (N = 12) and people diagnosed with ASD (N = 12), were assessed with the Observer Alexithymia Scale (OAS) by Haviland et al., 2000. The mean age of the neurotypical is (M = 21.67; SD = 2.60) and the ASD is (M = 18.33; SD = 2.22). Using SPSS ver.20, the data was analysed using descriptive and inferential statistics methods. The results indicate the significant difference between neurotypical and autism spectrum disorder individuals with the condition of alexithymia. The path model, which was drawn from the SPSS AMOS version 20, emphasises the causal relationship between variables of interest from the Observer Alexithymia Scale. This study found that individuals with ASD have significant corroboration to alexithymia when compared to neurotypical individuals.

Altruism under Stress: Cortisol Negatively Predicts Charitable Giving and Neural Value Representations Depending on Mentalizing Capacity.

Altruism, defined as costly other-regarding behavior, varies considerably across people and contexts. One prominent context in which people frequently must decide on how to socially act is under stress. How does stress affect altruistic decision-making and through which neurocognitive mechanisms? To address these questions, we assessed neural activity associated with charitable giving under stress. Human participants (males and females) completed a charitable donation task before and after they underwent either a psychosocial stressor or a control manipulation, while their brain activity was measured using functional magnetic resonance imaging. As the ability to infer other people's mental states (i.e., mentalizing) predicts prosocial giving and may be susceptible to stress, we examined whether stress effects on altruism depend on participants' general capacity to mentalize, as assessed in an independent task. Although our stress manipulation per se had no influence on charitable giving, increases in the stress hormone cortisol were associated with reductions in donations in participants with high mentalizing capacity, but not in low mentalizers. Multivariate neural response patterns in the right dorsolateral prefrontal cortex (DLPFC) were less predictive of postmanipulation donations in high mentalizers with increased cortisol, indicating decreased value coding, and this effect mediated the (moderated) association between cortisol increases and reduced donations. Our findings provide novel insights into the modulation of altruistic decision-making by suggesting an impact of the stress hormone cortisol on mentalizing-related neurocognitive processes, which in turn results in decreased altruism. The DLPFC appears to play a key role in mediating this cortisol-related shift in altruism.SIGNIFICANCE STATEMENT Altruism is a fundamental building block of our society. Emerging evidence indicates a major role of acute stress and stress-related neuromodulators in social behavior and decision-making. How and through which mechanisms stress may impact altruism remains elusive. We observed that the stress hormone cortisol was linked to diminished altruistic behavior. This effect was mediated by reduced value representations in the right dorsolateral prefrontal cortex and critically depended on the individual capacity to infer mental states of others. Our findings provide novel insights into the modulation of human altruism linked to stress-hormone dynamics and into the involved sociocognitive and neural mechanisms, with important implications for future developments of more targeted interventions for stress-related decrements in social behavior and social cognition.